Editor's note
Wildlife, wildfire, biomass: better data arrives.
This edition features several examples of rough proxies being replaced by direct measurement. Wildlife inventory moves from pellet counts to thermal imaging. Forest fuel potential goes from estimates to a 10-metre national grid. Wildfire detection shifts from human spotting to 17-minute satellite alerts.
The common thread is resolution. When you measure more precisely, and across more variables, you change what can be managed. That applies whether the subject is ungulate populations, biomass extraction or fire response.
Axel
WHAT GOT ME THINKING
Thermal Drones Replace Pellet Counts for Multi-Species Wildlife Inventory
Swedish startup Viltförvaltning.se offers AI-driven wildlife inventory using drones equipped with thermal cameras. The service maps all large game across a property in a single session, delivering species-level counts, movement patterns and damage assessments within three days. The approach replaces manual methods where volunteers count moose droppings on sample plots over an entire winter season to estimate density for a single species.
Axel's notes: Sweden's wildlife inventory tools were designed to count one species: moose. They work, but with known limitations: pellet counts carry a margin of error of ±25 percent and require a density change of two to three moose per thousand hectares before a trend is even detectable. Meanwhile, the species that have expanded most in recent decades, wild boar, fallow deer and red deer, are largely invisible to these methods. Total large game harvest in Sweden has grown over 50 percent since 2006, but the management toolkit still centres on one species.
That matters more than it might seem. Research from SLU shows that in southern Sweden, roe deer density is a stronger predictor of pine browsing damage than moose density itself. When multiple deer species compete for the same forage, moose shift their diet toward pine, and the damage equation changes. A management system that only counts moose will misattribute the cause.
What makes thermal drone inventory interesting is that a single session captures the full large-game picture: moose, roe deer, red deer and wild boar together, with species separation handled by AI on the thermal and optical imagery. The method has its own margin of error, mainly because animals move between the drone's flight lines, but the team mitigates this by flying multiple drones simultaneously. For the first time, a forest owner can base decisions on the actual composition of wildlife on the property, not an extrapolation from one species.
The practical implications are worth watching. If multi-species inventory becomes routine and affordable, it changes the input data for harvest planning, browsing damage mitigation and road traffic safety measures where wildlife crossings are a factor. The foundation for better wildlife management has always been better data. This looks like a step toward that.
Mellanskog Selects Skogshubben for Next-Generation Forest Planning
Mellanskog, one of Sweden's largest forest owner associations, has chosen Skogshubben's platform for its future forest planning operations. Skogshubben, developed by Ecotype, provides GIS-based tools for establishing, maintaining and updating forest management plans, with seamless transitions between office and field work. The move signals that major cooperatives are investing in dedicated digital planning infrastructure rather than building it in-house.
Wageningen PhD Thesis Uses AI to Decode Why Tropical Forests Disappear
Bart Slagter's doctoral thesis at Wageningen University combines Sentinel-1 radar and Sentinel-2 optical data with deep learning to detect not just where tropical tree cover is lost, but what is driving it: agriculture, selective logging, road development or wildfire. Applied wall-to-wall across the tropics and in detail across the Congo Basin, the methods revealed that up to 10,000 km of new logging roads are opened annually in the region, with an estimated 14.7 million m3 of timber harvested each year. The near real-time driver attribution makes satellite alerts substantially more actionable for conservation enforcement.
SMHI Validates Sweden's Satellite Wildfire Detection System in New Paper
A paper published in the inaugural issue of the Journal of Pyrogeography validates the automated wildfire detection system that SMHI operates for Sweden's emergency services. Built after the severe 2018 fire season, the system processes VIIRS satellite data and sends alerts directly to SOS Alarm with an average latency of 17 minutes. Over 2022 to 2024, satellites were the first to detect the fire in 29 percent of cases. The next upgrade comes from the Metop-SG-A1 satellite, launched in August 2025, which carries the METimage instrument for improved detection.
Skogforsk Builds Sweden's First National Biomass Fuel Map at 10-Metre Resolution
Within the Energimyndigheten-funded project "Good to go Grot", Skogforsk is building an interactive national map showing where logging residue extraction is both technically possible and ecologically realistic. The grot grid combines forest basic data, species distribution, tree age, slope, soil moisture and constraint layers like key biotopes into a 10-metre resolution raster. Once launched as a web application, it will give energy companies, forest owners and planners a shared picture of Sweden's available forest fuel potential, supporting the transition away from fossil energy.